Length: 3 Days
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Decision Analysis Training  Workshop

A key component to decision analysis methodology is the expected value.

Expected value is a commonly used financial concept. In finance, it indicates the anticipated value of an investment in the future. Expected value (also known as EV, expectation, average, or mean value) is a long-run average value of random variables.

It also indicates the probability-weighted average of all possible values. By determining the probabilities of possible scenarios, one can determine the EV of the scenarios. The concept is frequently used with multivariate models and scenario analysis. It is directly related to the concept of expected return.

After a model is constructed, it is important to find the expected value (EV) to evaluate which decision results in the most favorable outcome.

Local health departments, pharmaceutical companies, or other agencies can use decision analysis for internal decision-making processes.

Decision analysis is often used by non-health businesses interested in deciding whether they should release a product, perform internal restructuring, and so forth.

One great strength of decision analysis modeling is that it allows for the calculation of a range of possible values around a given mean. This approach, called “sensitivity analysis,” allows the user to better understand the chances that he or she will make a bad decision if a given strategy is taken.

Decision analysis, like cost-effectiveness analysis, is highly dependent on the accuracy and completeness of model inputs, as well as the assumptions that the analysts make.

Part of the decision analysis process requires examining potential uncertainties surrounding a decision. Organizations may need to conduct research or other analysis to determine the probabilities of different outcomes. You can assess your decision based on the likelihood of its success and its ensuing potential value—or the likelihood of its failure and the corresponding potential loss.

Analysts often recommend a step by step process in order to more effectively carry out decision analysis. Perhaps the most important step is to identify the problem.

Once you identify the issue, think about the options or solutions available to you. For example, say you received a large sum of money and want to invest it. You may have several investment options to choose from and performing a decision analysis can help you choose the one that best suits your needs.

Those options then need to be researched. This information is important because it provides data organizations can use when developing a decision model and measuring the options’ outcomes. Options should be explored from different perspectives, such as:

  • Associated costs
  • Risks
  • Trade-offs
  • Benefits
  • Odds of success

Ultimately, the decision analysis process comes down to finding the expected values of your options. The expected value represents the average outcome of each decision. To complete this step, you must assign monetary or numerical values to each outcome and determine the probability of each.

Generally, the expected value is found by multiplying the outcome value of each option by its probability. This step provides you the partial value of each outcome. You then need to add up the partial values, and the result represents your expected value.

Organizations assess these values against their framework to determine which option best meets a company’s needs.

Decision Analysis Training Workshop Course by Tonex

Decision Analysis Training Workshop, Introduction Decision Analysis is a 3-day training workshop designed for professionals in engineering, business, innovative technology, defense and aerospace, medicine and other fields. Decision analysis is a powerful way to think through and analyze decision problems involving uncertainty, complexity and time.

Participants will learn about decision analysis and how it can help when it comes to a tough decision by structuring the problem in terms of alternatives, information and preferences. Uncertainties and tradeoffs are made explicitly and allows decision makers to clarify their personal preferences with greater confidence.

Participants will learn the skills needed to participate in the application of Decision
Analysis to projects and programs.

In Class & Live Online Training

  • 2-day instructor led training course
  • Additional One-on-one support after the course up to 6 months

Decisions are choices between alternative courses of action:

  • Involves managing uncertain outcomes
  • Involves tradeoffs between different benefits

Learning Objectives

After completing this course, the participants will be able to:

  • Describe the decision-making environments of certainty and uncertainty.
  • Define parameters for your decision analysis task, project or program
  • Step through the life of a decision analysis process
  • Construct decision tree diagrams.
  • Construct both a payoff table and an opportunity-loss table.
  • Apply root cause analysis and cause and effect principles (5-why’s, Fishbone/Ishikawa diagrams)
  • Identify key principles of network analysis, modeling and simulation (Monte Carlo)
  • Define the expected value criterion using forecasting techniques.
  • Apply the expected value criterion in decision situations.
  • Compute the cost of uncertainty and value of perfect information.
  • Develop a decision tree and explain how it can aid decision making in an uncertain environment.

course details

Decision Analysis 101

  • What is Decision Analysis?
  • Applying Decision Analysis
  • Why are Decisions Difficult?
  • Consequences, Uncertainty, and Ambiguity
  • A Scalable Process: Uncertainty and Ambiguity
  • Real World Decisions
  • The Role of Decision Analysis
  • Decision Analysis Process
  • Decision Making in a Complex Scenarios
  • Differentiation Between Ambiguity and Uncertainty
  • Engagement in a Project or Strategy

Framing Decision Problems and Scenarios

  • Modeling Preferences and Decision Analysis Phases
  • Measuring Uncertainty
  • Decision Strategies and Confidence Through Clarity
  • Decision Management
  • Interpretation to Gain Insight and Agreement
  • Tools and Techniques
  • Decision Trees
  • Group Decision Making
  • Root-Cause Analysis
  • Risk Analysis
  • Program and Project Evaluation
  • Conflict Analysis
  • Rapid Analysis
  • Benchmarking
  • Judgement of Decision Quality and Effectiveness

Uncertainty and Making Choices

  • Decisions and Uncertainty
  • Measures of Merit
  • Time Value of Money
  • Dealing with Risk
  • The Certain Equivalent
  • Principles of Evaluations
  • Using Distinctions
  • Defining Possibilities

Making Compelling Decisions

  • The Decision Elements
  • Why We Have Difficulty Achieving High-Quality Decisions
  • How Do You Achieve Decision Quality?
  • The Ten Principles Of Good Decision-Making
  • How Do You Measure Decision Quality?
  • The Scalable Decision Process (SDP)
  • Structuring Phase
  • Evaluation Phase
  • Agreement Phase

Creating a Shared Understanding of the Problem

  • Framing the Problem
  • The Participants in the Process
  • Developing an Appropriate Frame
  • Creating Alternatives
  • Preparing for Evaluation

Developing a Decision Model

  • Building Influence Diagrams
  • Decision Trees
  • Computer Modeling Programs
  • What is Probability?
  • Probability Basics
  • Venn Diagrams
  • States of Information
  • Probability Trees
  • Reversing the Tree
  • Using and Understanding Distributions

Using Simulation to Solve Decision Problems

  • What is a Monte Carlo Simulation?
  • Why Use Monte Carlo Simulation?
  • Using Random Numbers to Simulate Reality
  • Using the Results of a Monte Carlo
  • Commercial Software
  • The Role of Monte Carlo
  • Working with a Real Monte Carlo Simulation (Using Anaconda python toolbox)

Using Uncertain Information and Judgment

  • Using Limited Information
  • Gathering Information
  • Uncovering and Dealing with Biases
  • Assessing Information
  • Using Probability as the Language of Uncertainty
  • Discretizing the Information
  • Gaining Insight Through Evaluation
  • Getting to Agreement
  • Using the Scalable Decision Process on Large Projects
  • Portfolio Analysis and Management

Workshop 1

  • Implementing the Decision Analysis Process
  • Implementing Decision Analysis
  • What is Right for Your Organization?
  • Implementation Issues
  • Real World Problems
  • Implications and Reactions
  • Decision Response Inventory Exercise (DRIVE)
  • Facilitation and Analysis Summaries
  • Eliciting Issues
  • Decision Hierarchy
  • Influence Diagrams
  • Strategy Table
  • Assessment
  • Decision Trees
  • Decision Quality Radar Chart

Supplementary Course Book:

Introduction to Decision Analysis, 3rd Edition

A Practitioner’s Guide to Improving Decision Quality by David C. Skinner, Edited by Paul Wicker

Introduction to Decision Analysis by David Skinner

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